There are several benchmark datasets collected to promote the exploration of instance segmentation. The COCO [1]} and the Cityscapes [2]} are the most popular ones among them. However, the shapes of target objects in these datasets are too regular. The connected components per instance (CCPI) and average MaxIoU are low in the datasets and state-of-the-art algorithms selected from them can not generalize to more challenging scenarios. Instead, in the scenario of human detection and segmentation, the OC human [3]} and the Crowd Human [4]} introduce datasets with larger MaxIoU. Nevertheless, the OC human dataset only provides a small number of images for testing, and the number of instances per image is too small to challenge instance segmentation algorithms. While the crowd human dataset only provides annotations of object bounding boxes, limiting their application to the instance segmentation field. In the area of photogrammetry, the iSAID [5]} dataset is proposed to lead algorithms to tackle objects with multi scales. However, only a few instances in this dataset are of irregular shape, most of which are rectangular, and the lack of instance overlapping reduces its challenge to instance segmentation algorithms as well. Under the observation that these existing regular datasets are not enough to challenge algorithms for more general scenarios, we propose iShape, which contains irregularly shaped objects with large overlaps between bounding boxes of objects, extreme aspect ratios, and large numbers of CCPI to promote the capabilities of instance segmentation algorithms.
